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Abstract Predicting breast cancer recurrence risk is a critical clinical challenge. This study investigates the potential of computational pathology to stratify patients using deep learning on routine hematoxylin and eosin (H&E) stained whole-slide images (WSIs). We developed and compared three multiple instance learning (MIL) frameworks—CLAM-SB, ABMIL, and ConvNeXt-MIL-XGBoost—on an in-house dataset of 210 patient cases.
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
